Environmental indicator analysis-Tilefish

# Loading packages and data
library(ggplot2)
library(ecodata)
library(lubridate)
library(dplyr)
library(stringr)
library(marmap) # bathymetry
library(RColorBrewer)
library(ggnewscale)
library(sf)
library(cowplot)
library(tidyverse)
library(ggpubr)
library(sf)
library(ggdist)
library(ggpubr)
library(wesanderson)
library(raster)
#library(glmTMB)

# CPUE data (no env covariates)
gt_data_model_cpue <- read.csv(here::here('data/catch_data/gt_data_model_cpue.csv'))
#names(gt_data_model_cpue) <- tolower(names(gt_data_model_cpue))
# Add in column with cpue 
# note: Paul indicated to use small mesh
gt_data_model_cpue <- gt_data_model_cpue %>% 
       rename_all(., .funs = tolower) %>% 
  mutate(mesh_bin = case_when(mesh_size <= 5.6 ~ 'SM',mesh_size >= 5.6 ~ 'LG',
                                                      TRUE ~ 'NA')) %>%
  mutate(cpue_hr = sum_gt_catch/effort_dur)

# Catch data: 
sfobs <-readRDS(here::here('data/catch_data/gold_tile_sf_ob_v1_temp_price.rds'))

sfob.env <- sfobs %>% 
  mutate(mesh_bin = case_when(mesh_size <= 5.6 ~ 'SM', mesh_size >= 5.6 ~ 'LG',
                              TRUE ~ 'NA'),
         cpue_hr = SUM_GT_CATCH/effort_dur) %>% 
  filter(YEAR %in% c(1998:2022) & mesh_bin == 'SM') %>%
  dplyr::select(DATE, YEAR, MONTH, YDAY,trip_id,hull_num, area, effort_dur, 
         SUM_GT_CATCH, cpue_hr, mesh_size, mesh_bin, depth, start_lat, start_lon,
         bottomT, bottomT_avg, MIN_TEMP_C, MEAN_TEMP_C, MAX_TEMP_C,
         TEMP_VARIANCE, TEMP_DEVIATION, MEAN_DPTH_M, tri, sed) %>% 
  mutate(YEAR = as.integer(YEAR)) %>% 
  rename_all(., .funs = tolower)

write.csv(sfob.env, "C:/Users/stephanie.owen/Documents/tilefish_indicators/data/catch_data/sfob_env.csv", row.names=FALSE)


areas <- sort(unique(sfob.env$area))
catch.tally.ann <- sfob.env %>% # aggregate by year
  group_by(year) %>% 
  summarise(ttl_sum = sum(sum_gt_catch))

# Length data from observer program 
lengths <- read.csv(here::here('data/catch_data/gt_data_length_andy.csv')) 
names(lengths) <- tolower(names(lengths))

# Recruitment estimates from 2021 report
recruit <- read.csv(here::here('data/assessment_data/tilefish_rec_estimate_2021.csv'))

# Merge SF/Obs catch data with recruit estimates:
catch_recruit <- cbind(recruit %>% filter(year %in% c(1998:2020)),
                       catch.tally.ann %>%
                         filter(year %in% c(1998:2020)) %>%
                         dplyr::select(ttl_sum))

# loading in shape files for maps
wd = here::here('shapefiles')
US.areas <- st_read(here::here('shapefiles/USA.shp'), quiet = TRUE)
canada.areas <- st_read(here::here('shapefiles/Canada.shp'), quiet = TRUE)
bts_strata <- st_read(here::here('shapefiles/NES_BOTTOM_TRAWL_STRATA.shp'),
                      quiet = TRUE)
# plot(bts_strata) # to see all bottom trawl strata

gtf_strata <- bts_strata %>% 
  filter(STRATUMA %in% c('01030', '01040', '01070', '01080', '01110', '01120', 
                         '01140', '01150', '01670', '01680', '01710', '01720', 
                         '01750', '01760')) # select just the gtf strata
# plot(gtf_strata)
bathy <- marmap::getNOAA.bathy(-81,-58, 27, 46)
bathy = fortify.bathy(bathy)

Tilefish data

---

Catch data

Year-class strength is broadly defined as the number of fish spawned or hatched in a given year (Ricker, 1975).

Figure 1. Sum of catch (not accounting for effort), across years. Light blue shaded region represents the temporal range of observer records and red shaded region represents temporal range of study fleet records. The ‘purple’ region is where they overlap. Note that 2000-2005 for observer records had low sample size/number of vessels for tilefish, making the shaded region likely the best region to use for analysis. The vertical dashed lines represent strong year classes for this species (Nesslage et al. 2021). Red asterisk marks year that stock was deemed ‘re-built’.

# tot_catch == total (sum_catch) across hauls. so if tallying up annually, 
# use sum_catch
# Strong year-classes: 1970, 1973, 1993, 1999, 2005, 2013

ggplot(catch.tally.ann, aes(x = factor(year), y = ttl_sum, group = 1))+
   geom_rect(aes(xmin = '2007', xmax = '2022', ymin = -Inf, ymax = Inf), 
            fill = 'red', alpha = 0.02) +
  geom_rect(aes(xmin = '2000', xmax = '2022', ymin = -Inf, ymax = Inf), 
            fill = 'lightblue', alpha = 0.05) +
   geom_vline(xintercept = c('1993','1999', '2005', '2013'), lty = 2) +
  geom_line(color = 'black', size = 1.5) +
  annotate("text", label = "*",
    x = 26, y = 14000, size = 8, colour = "red" )+
  xlab('Year') + 
  ylab('Total sum tilefish catch') + 
  # facet_wrap(~month)+
  theme(axis.text.x = element_text(color = 'black',
                                   size = 12, angle = 45, vjust = 1, hjust=1)) +
  ecodata::theme_facet()

CPUE

Figure 2. Catch-per-unit-effot for undirected trawl trips from the Study fleet and observer program. Zeros have been added using species association methodology (via jaccard index).

see here for example

gt_data_model_cpue %>% 
  filter(mesh_bin == 'SM') %>% # note: Paul indicated to use small mesh
  group_by(year, source) %>% 
  summarise(mean_cpue = mean(cpue_hr),.groups = 'drop') %>%
  ggplot(aes(x=year,y=mean_cpue)) +
  geom_line(lwd = 1) +
  facet_wrap(~source) + 
  theme_bw()

gt_data_model_cpue %>% 
  filter(mesh_bin == 'SM') %>%
  group_by(year) %>% 
  summarise(mean_cpue = mean(cpue_hr),.groups = 'drop') %>%
  ggplot(aes(x=year,y=mean_cpue)) +
  geom_line(lwd = 1) +
  labs(title = 'Study fleet + Observer combined') + 
  theme_bw()

Maps (catch)

Tilefish catch locations (study fleet/observer)

yrs = sort(unique(gt_data_model_cpue$year))    

#for(i in 1:length(yrs)){
yrmap <- function(yrs){
  gt_data_model_cpue %>% 
  filter(start_lat < 42.5 & depth_est > 50 & year == yrs) %>%
  mutate(bin = cut(year, seq(min(year), max(year) + 4, 4), right = FALSE)) %>%
  ggplot() + 
  geom_sf(data = US.areas %>% st_as_sf(),color = 'gray20', fill = '#cbdbcd') +
  geom_contour(data = bathy,
               aes(x=x,y=y,z=-1*z),
               breaks=c(50,100,150,200, Inf),
               size=c(0.3),
               col = 'darkgrey') +
  stat_summary_2d(aes(x=start_lon, y=start_lat, z = cpue_hr),
                  binwidth=c(0.16666,0.16666)) + 
  scale_fill_viridis_c() + 
  theme(legend.position = "bottom",
        legend.key.size = unit(0.2, "cm"),
        legend.key.width = unit(1, "cm")) +
  coord_sf(xlim = c(-75,-65.5), ylim = c(36,44), datum = sf::st_crs(4326))  +
  labs(x = '', y = '', fill = 'CPUE') +
  theme_bw() 
}


for(i in 1:length(yrs)){
 cat("\n#####",  as.character(yrs[i]),"\n")
    print(yrmap(yrs[i])) 
    cat("\n")   
}

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Recruitment estimate

Figure 3. Age-1 recruitment estimate from the 2021 tilefish assessment across all years

ggplot(recruit, aes(x = factor(year), y = recruit_est, group = 1))+
   geom_rect(aes(xmin = '2007', xmax = '2022', ymin = -Inf, ymax = Inf), 
            fill = 'red', alpha = 0.02) +
  geom_rect(aes(xmin = '2000', xmax = '2022', ymin = -Inf, ymax = Inf), 
            fill = 'lightblue', alpha = 0.05) +
   geom_vline(xintercept = c('1993','1999', '2005', '2013'), lty = 2) +
  geom_line(color = 'black', size = 1.5) +
  annotate("text", label = "*",
    x = 26, y = 14000, size = 8, colour = "red" )+
  xlab('Year') + 
  ylab('Total sum tilefish catch') + 
  # facet_wrap(~month)+
  theme(axis.text.x = element_text(color = 'black',
                                   size = 12, angle = 45, vjust = 1, hjust=1)) +
  ecodata::theme_facet()

### Thought: Should we isolate years associated w/strong year classes (or bad)
###           for correlations and analyses? 

Figure 4. Recruitment estimates in focus years

• Paul has suggested that data used to calculate recruitment estimates was strongest from 2000 to present.
• Extended to 1998 to capture both the strong year class and El nino associated with that year.

ggplot(recruit %>% filter(year %in% c(1998:2022)),
       aes(x = factor(year), y = recruit_est, group = 1))+
  geom_vline(xintercept = c('1993','1999', '2005', '2013'), lty = 2) +
  geom_line(color = 'black', size = 1.2) +
  xlab('Year') + 
  ylab('Recruit estimates') + 
  theme(axis.text.x = element_text(color = 'black',
                                   size = 12, angle = 45, vjust = 1, hjust=1)) +
  ecodata::theme_facet()

Figure 5. Recruitment estimates and Study Fleet and Observer catch data. Black line denotes recruitment estimate, yellow denotes sum of annual catch data across both Study fleet and Observer programs.

options(scipen=999)
ggplot(catch_recruit) +
  geom_line(aes(x = factor(year), y = recruit_est, group = 1),
            col = 'black', size = 1.2) +
  geom_line(aes(x = factor(year), y = ttl_sum*1000), size = 1.2, 
            color = 'goldenrod1', group = 1) +
  scale_y_continuous(sec.axis = sec_axis(~./1000, name = 'Catch (lbs)')) + 
  geom_vline(xintercept = c('1993','1999', '2005', '2013'), lty = 2) +
  xlab('Year') + 
  ylab('Recruit estimates') + 
   theme(axis.text.x = element_text(color = 'black',
                                   size = 12, angle = 45, vjust = 1, hjust=1)) +
  ecodata::theme_facet()

Length data

All lengths

Figure 6. Distribution of lengths Figure 7. Length frequencies Figure 8. Frequency of smaller individuals

# Define category breaks
size_breaks <- c(0,10,20,30,40, 50, 60, 70, 80, 90, 100)
# Making a function to bin the catches
label_interval <- function(breaks) {
  paste0("(", breaks[1:length(breaks) - 1], "-", breaks[2:length(breaks)], ")")
}
labels = label_interval(size_breaks)

# length freq. table
tab = table(cut(lengths$lenanml, 
    breaks = size_breaks,
    labels = label_interval(size_breaks)))

## Plot full distribution
ggplot(lengths,
       aes(x = lenanml)) + 
  geom_bar(position = position_dodge(), 
           alpha = 0.4, fill= 'blue', color="black") + 
  xlab('Tilefish length (cm)') +
  theme_bw() +
  theme_facet()

# Plot length frequencies
barplot(tab, xlab = 'Length bins (mm)', main = '')

# Just the little ones
barplot(tab[1:3], xlab = 'Length bins (mm)', main = '')

Juveniles

Young of year - year 1 and 2 size class

ggplot(lengths %>% filter(lenanml <= 26),
       aes(x = lenanml)) + 
  geom_bar(position = position_dodge(), 
           fill= 'slateblue', color="black") + 
  xlab('Tilefish length (cm)') +
  theme_bw() +
  theme_facet()

ggplot(lengths %>% filter(lenanml <= 26),
       aes(x = lenanml, fill = numlen)) + 
  geom_bar(position = position_dodge(), 
           alpha = 0.4, fill= 'blue', color="black") + 
  xlab('Tilefish length (cm)') +
  theme_bw() +
  facet_wrap(~year) + 
  theme_facet()

Environmental data

---

The strong year classes for Golden Tilefish were 1993, 1998, 2005, 2013. Some of the underlying oceanographic processes that may be related to recruitment may influence habitat, retention/displacement and food availablity. These are explored below.

Habitat

Tilefish occupy a very narrow band of habitat conditions. Therefore, temperature and salinity may be of interest.

SST

# SST

Maps (SST)

SST analysis

Bottom Temperature

Figure 1. GLORYS vs in-situ bottom temperatures from study fleet vessels.

Figure 2. Bottom temperature (C) across years. Blue dots are in-situ data, red dots are from GLORYS.

ggplot2::ggplot(sfob.env, aes(x=bottomt, y=mean_temp_c)) +
  geom_point(color="blue", alpha=0.1)+
  geom_abline(intercept = 0, slope = 1) +
  xlab('Bottom Temp (SF)') +
  ylab('Bottom Temp (GLORYS)') +
  theme_bw() 

ggplot2::ggplot(sfob.env, aes(x=bottomt, y=year)) +
  geom_point(color="blue", alpha=0.1) +
  geom_point(data = sfob.env, aes(x=mean_temp_c, y=year),
             color="red", alpha=0.1) +
  xlab('Bottom Temp') +
  ylab('Year') +
  labs(color = 'Source') +
  theme_bw() 

Maps (Bottom T)

jet.colors <-colorRampPalette(c("blue", "#007FFF", "cyan","#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"))

# select just years with study fleet bottom temps
sf.bt <- sfob.env %>% filter(year>2006 & depth > 50) 
yrs = sort(unique(sf.bt$year))    

#for(i in 1:length(yrs)){
yrmap <- function(yrs){
  sf.bt %>% filter(year == yrs) %>% 
  ggplot() + 
  geom_sf(data = US.areas %>% st_as_sf(),color = 'gray20', fill = '#cbdbcd') +
  geom_contour(data = bathy,
               aes(x=x,y=y,z=-1*z),
               breaks=c(50,100,150,200, Inf),
               size=c(0.3),
               col = 'darkgrey') +
  stat_summary_2d(aes(x=start_lon, y=start_lat, z = bottomt),
                  binwidth=c(0.16666,0.16666)) + 
    scale_fill_gradientn(colors = jet.colors(20)) +
  coord_sf(xlim = c(-75,-65.5), ylim = c(36,44), datum = sf::st_crs(4326))  +
  labs(x = '', y = '', fill = 'Bottom temperature (°C)') +
  theme_bw() 
}


for(i in 1:length(yrs)){
 cat("\n######",  as.character(yrs[i]),"\n")
    print(yrmap(yrs[i])) 
    cat("\n")   
}

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Bottom T analysis

The following figures compare in-situ bottom temperature from the study-fleet data set to the recruitment estimate.

• Note here temperatures are averaged across all depths > 50 for each month.

# Note here temperatures are averaged across all depths > 50 for each month.

# Create in-situ bottom temps by month w/lag
df.lag = sfob.env %>% filter(year > 2006 & depth > 50) %>%
  group_by(year,month) %>% 
  summarise(mean_dpth = mean(depth),
            mean_bt = mean(bottomt)) %>% 
  mutate(mean_bt_lag2 = lag(mean_bt,2),
         mean_bt_lag3 = lag(mean_bt,3), 
         mean_bt_lag6 = lag(mean_bt, 6))
# Join in-situ bottom temps w/assessment recruitment estimate
df.join = dplyr::full_join(recruit, df.lag, by = join_by(year)) %>%
  dplyr::select(year, month, recruit_est, mean_dpth, 
                mean_bt, mean_bt_lag2, mean_bt_lag3, mean_bt_lag6) %>%
  tidyr::drop_na()
# See what months have data
sort(unique(df.join$month))

## [1]  7  8  9 10 11 12

hist(df.join$month) # will group into spring/summer fall/winter categories

## spring/summer bottom temp no lag
ggplot2::ggplot(df.join %>% filter(month %in% c(4,5,6,7,8)),
                aes(x=recruit_est, y=mean_bt)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean bottom temp (°C)')+
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## spring/summer bottom temp no lag
ggplot2::ggplot(df.join %>% filter(month %in% c(9,10,11,12)),
                aes(x=recruit_est, y=mean_bt)) + 
  geom_point(color= 'black')+
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean bottom temp (°C)')+
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## spring/summer bottom temp 2 month lag
ggplot2::ggplot(df.join %>% filter(month %in% c(4,5,6,7,8)),
                aes(x = recruit_est, y = mean_bt_lag2)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean bottom temp (°C)')+
  labs(title = 'Lag 2 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## spring/summer bottom temp 2 month lag
ggplot2::ggplot(df.join %>% filter(month %in% c(9,10,11,12)),
                aes(x = recruit_est, y = mean_bt_lag2)) + 
  geom_point(color= 'black')+
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean bottom temp (°C)')+
  labs(title = 'Lag 2 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## spring/summer bottom temp 3 month lag
ggplot2::ggplot(df.join %>% filter(month %in% c(4,5,6,7,8)),
                aes(x = recruit_est, y = mean_bt_lag3)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean bottom temp (°C)')+
  labs(title = 'Lag 3 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## spring/summer bottom temp 3 month lag
ggplot2::ggplot(df.join %>% filter(month %in% c(9,10,11,12)),
                aes(x = recruit_est, y = mean_bt_lag3)) + 
  geom_point(color= 'black')+
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean bottom temp (°C)')+
  labs(title = 'Lag 3 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## spring/summer bottom temp 6 month lag
ggplot2::ggplot(df.join %>% filter(month %in% c(4,5,6,7,8)),
                aes(x = recruit_est, y = mean_bt_lag6)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean bottom temp (°C)')+
  labs(title = 'Lag 6 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## spring/summer bottom temp 6 month lag
ggplot2::ggplot(df.join %>% filter(month %in% c(9,10,11,12)),
                aes(x = recruit_est, y = mean_bt_lag6)) + 
  geom_point(color= 'black')+
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean bottom temp (°C)')+
  labs(title = 'Lag 6 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

The following figures compare in-situ bottom temperature from the study-fleet data set to the Study fleet and Observer tilefish catch data.

• Note here temperatures are averaged across all depths > 50 for each month.

Stephanie I hope the code below is a good jumping off point!

# Stephanie here I subset the years that are relevant to the Study fleet 
# data set. Note that Paul suggested looking at catch that is from depths 
# greater than 50 m and only from small mesh gear. The object sfob.env is
# already filtered for just small gear (see line 57 above).


# You can run this line just to check. 
unique(sfob.env$mesh_bin)

## [1] "SM"

# the lines that create the df.lag object selects  depths > 50, just note 
# that there are many depths here, so this is taking the average temperature
# across all depths. We may want to think a bit more about how we want to 
# subset for averaging (or not?)

# here is a way to see what depths are reported 
sort(unique(sfob.env$depth))

##   [1]    0.00000    2.00000    3.00000    4.00000    5.00000    6.00000
##   [7]    7.00000    8.00000    9.00000   10.00000   11.00000   12.00000
##  [13]   13.00000   14.00000   15.00000   16.00000   17.00000   18.00000
##  [19]   19.00000   20.00000   21.00000   22.00000   23.00000   24.00000
##  [25]   25.00000   26.00000   27.00000   28.00000   29.00000   30.00000
##  [31]   31.00000   31.71485   32.00000   33.00000   34.00000   35.00000
##  [37]   36.00000   37.00000   38.00000   39.00000   39.37016   40.00000
##  [43]   41.00000   42.00000   43.00000   43.74462   44.00000   44.83823
##  [49]   45.00000   45.93185   46.00000   46.47866   47.00000   48.00000
##  [55]   48.11908   49.00000   49.21270   50.00000   50.30631   51.00000
##  [61]   51.39993   51.94674   52.00000   53.00000   53.04035   54.00000
##  [67]   54.13397   54.68077   55.00000   55.33344   56.00000   57.00000
##  [73]   57.83345   58.00000   59.00000   60.00000   60.14885   60.69566
##  [79]   61.00000   62.00000   63.00000   64.00000   64.50013   65.00000
##  [85]   66.00000   66.33347   67.00000   68.00000   68.35097   69.00000
##  [91]   70.00000   71.00000   72.00000   73.00000   73.83348   74.00000
##  [97]   74.36585   74.91266   75.00000   75.45947   76.00000   76.55308
## [103]   77.00000   78.00000   78.16682   78.19351   78.33349   79.00000
## [109]   79.28712   80.00000   80.38074   80.83349   80.92755   81.00000
## [115]   81.00016   81.47435   82.00000   82.02116   83.00000   83.00017
## [121]   83.11478   83.50017   83.66158   84.00000   84.20839   84.33350
## [127]   84.75520   85.00000   85.84882   86.00000   86.83351   87.00000
## [133]   87.16684   87.48924   88.00000   88.03605   89.00000   89.67647
## [139]   90.00000   90.77009   91.00000   91.31689   92.00000   93.00000
## [145]   93.50412   94.00000   95.00000   96.00000   97.00000   98.00000
## [151]   98.83353   99.00000  100.00000  100.50020  100.66687  101.00000
## [157]  101.00020  101.50020  102.00000  103.00000  104.00000  105.00000
## [163]  105.16688  106.00000  106.66688  107.00000  108.00000  109.00000
## [169]  110.00000  111.00000  112.00000  113.00000  114.00000  115.00000
## [175]  116.00000  117.00000  118.00000  119.00000  120.00000  121.00000
## [181]  122.00000  123.00000  124.00000  125.00000  126.00000  127.00000
## [187]  128.00000  129.00000  130.00000  131.00000  132.00000  133.00000
## [193]  134.00000  135.00000  136.00000  138.00000  139.00000  140.00000
## [199]  141.00000  142.00000  143.00000  144.00000  145.00000  146.00000
## [205]  147.00000  148.00000  149.00000  150.00000  151.00000  152.00000
## [211]  153.00000  154.00000  155.00000  158.00000  160.00000  161.00000
## [217]  163.00000  164.00000  165.00000  167.00000  168.00000  171.00000
## [223]  172.00000  176.00000  177.00000  180.00000  181.00000  182.00000
## [229]  184.00000  185.00000  186.00000  187.00000  188.00000  192.00000
## [235]  193.00000  196.00000  197.00000  199.00000  200.00000  203.00000
## [241]  204.00000  205.00000  206.00000  207.00000  208.00000  209.00000
## [247]  221.00000  228.00000  230.00000  231.00000  488.00000  500.00000
## [253]  622.00000  665.00000  677.00000  771.00000 1111.00000

# another note - since I group by year AND month - the lags are month specific. 
# If you wanted to lag by year only, you would have to get rid of month below.

#filter by year, depth, cpue (includes min, max, and sd of bottomt by month)
df.lag.cpue = sfob.env %>% filter(year > 2006 & depth > 50 & cpue_hr > 0) %>%
  group_by(year,month) %>% 
  summarise(mean_dpth = mean(depth),
            mean_bt = mean(bottomt),
            min_bt = min(bottomt),
            max_bt = max(bottomt),
            sd_bt = sd(bottomt),
            mean_cpue = mean(cpue_hr)) %>% 
  mutate(mean_bt_lag2 = lag(mean_bt,2),
         mean_bt_lag3 = lag(mean_bt,3), 
         mean_bt_lag6 = lag(mean_bt, 6))

#omit NA bottomt
df.lag.sfob <- df.lag.cpue[!(is.na(df.lag.cpue$mean_bt)), ]

# See what months have data
sort(unique(df.lag.sfob$month))

##  [1]  1  2  3  4  5  6  7  8  9 10 11 12

hist(df.lag.sfob$month)  #winter/spring and summer/fall; summer only season with any non-zero relationship, except fall with 6 mo lag

## Winter/spring bottom temp no lag
ggplot2::ggplot(df.lag.sfob %>% filter(month %in% c(1,2,3,4,5,6)),
                aes(x=mean_cpue, y=mean_bt)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Mean CPUE/hr') +
  ylab('Mean bottom temp (°C)') +
  labs(title = 'Winter/spring no lag') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Summer/fall bottom temp no lag
ggplot2::ggplot(df.lag.sfob %>% filter(month %in% c(7,8,9,10,11,12)),
                aes(x=mean_cpue, y=mean_bt)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Mean CPUE/hr') +
  ylab('Mean bottom temp (°C)')+
  labs(title = 'Summer/fall no lag') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Summer bottom temp no lag
ggplot2::ggplot(df.lag.sfob %>% filter(month %in% c(7,8,9)),
                aes(x=mean_cpue, y=mean_bt)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Mean CPUE/hr') +
  ylab('Mean bottom temp (°C)')+
  labs(title = 'Summer no lag') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Winter/spring bottom temp 2 month lag
ggplot2::ggplot(df.lag.sfob %>% filter(month %in% c(1,2,3,4,5,6)),
                aes(x = mean_cpue, y = mean_bt_lag2)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Mean CPUE/hr') +
  ylab('Mean bottom temp (°C)')+
  labs(title = 'Winter/spring lag 2 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Summer/fall bottom temp 2 month lag
ggplot2::ggplot(df.lag.sfob %>% filter(month %in% c(7,8,9,10,11,12)),
                aes(x = mean_cpue, y = mean_bt_lag2)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Mean CPUE/hr') +
  ylab('Mean bottom temp (°C)')+
  labs(title = 'Summer/fall lag 2 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Summer bottom temp 2 month lag
ggplot2::ggplot(df.lag.sfob %>% filter(month %in% c(7,8,9)),
                aes(x = mean_cpue, y = mean_bt_lag2)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Mean CPUE/hr') +
  ylab('Mean bottom temp (°C)')+
  labs(title = 'Summer lag 2 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Winter/spring bottom temp 3 month lag
ggplot2::ggplot(df.lag.sfob %>% filter(month %in% c(1,2,3,4,5,6)),
                aes(x = mean_cpue, y = mean_bt_lag3)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Mean CPUE/hr') +
  ylab('Mean bottom temp (°C)')+
  labs(title = 'Winter/spring lag 3 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Summer/fall bottom temp 3 month lag
ggplot2::ggplot(df.lag.sfob %>% filter(month %in% c(7,8,9,10,11,12)),
                aes(x = mean_cpue, y = mean_bt_lag3)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Mean CPUE/hr') +
  ylab('Mean bottom temp (°C)')+
  labs(title = 'Summer/fall lag 3 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Summer bottom temp 3 month lag
ggplot2::ggplot(df.lag.sfob %>% filter(month %in% c(7,8,9)),
                aes(x = mean_cpue, y = mean_bt_lag3)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Mean CPUE/hr') +
  ylab('Mean bottom temp (°C)')+
  labs(title = 'Summer lag 3 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Summer/fall bottom temp 6 month lag
# No 6 month lag data for winter/spring
ggplot2::ggplot(df.lag.sfob %>% filter(month %in% c(7,8,9,10,11,12)),
                aes(x = mean_cpue, y = mean_bt_lag6)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Mean CPUE/hr') +
  ylab('Mean bottom temp (°C)')+
  labs(title = 'Summer/fall lag 6 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Summer bottom temp 6 month lag
ggplot2::ggplot(df.lag.sfob %>% filter(month %in% c(7,8,9)),
                aes(x = mean_cpue, y = mean_bt_lag6)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Mean CPUE/hr') +
  ylab('Mean bottom temp (°C)')+
  labs(title = 'Summer lag 6 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Fall bottom temp 6 month lag
ggplot2::ggplot(df.lag.sfob %>% filter(month %in% c(10,11,12)),
                aes(x = mean_cpue, y = mean_bt_lag6)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Mean CPUE/hr') +
  ylab('Mean bottom temp (°C)')+
  labs(title = 'Fall lag 6 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

Corrleation and figures for sf/ob data and in-situ bottom temps

library("ggpubr")
lm_bt<-lm(mean_bt ~ mean_cpue, data=df.lag.sfob)
summary(lm_bt)

## 
## Call:
## lm(formula = mean_bt ~ mean_cpue, data = df.lag.sfob)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.6286 -1.0257  0.1391  1.1202  3.2579 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11.55520    0.24264  47.623 <0.0000000000000002 ***
## mean_cpue    0.03508    0.03154   1.112               0.268    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.72 on 122 degrees of freedom
## Multiple R-squared:  0.01004,    Adjusted R-squared:  0.001924 
## F-statistic: 1.237 on 1 and 122 DF,  p-value: 0.2682

#plot(mean_bt ~ mean_cpue, data=df.lag.sfob, pch=1, col="dodgerblue") + abline(lm_bt)

cor.test(df.lag.sfob$mean_cpue, df.lag.sfob$mean_bt, method=c("pearson"))

## 
##  Pearson's product-moment correlation
## 
## data:  df.lag.sfob$mean_cpue and df.lag.sfob$mean_bt
## t = 1.1123, df = 122, p-value = 0.2682
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.07749342  0.27170887
## sample estimates:
##       cor 
## 0.1001921

ggscatter(df.lag.sfob, x = "mean_cpue", y = "mean_bt", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.coeff.args = list(method="pearson", 
              label.x=20, label.y=7.5),
          add.params=list(color="dodgerblue", fill="lightgray"),
          xlab = "Mean CPUE/hr", ylab = "Mean bottom temp (°C)")

#ggqqplot(df.lag.sfob$mean_bt, ylab="Mean BT")
#ggqqplot(df.lag.sfob$mean_cpue, ylab="Mean CPUE")

#cor.test(df.lag.sfob$mean_cpue, df.lag.sfob$mean_bt, method=c("spearman"))

#ggscatter(df.lag.sfob, x = "mean_cpue", y = "mean_bt", 
         # add = "reg.line", conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = "spearman",
         # xlab = "Mean CPUE/hr", ylab = "Mean bottom temp (°C)")

Salinity

Here we explore salinity from the GLORYS reanalysis model at three different depths

• 55 meters (relevant to larvae, recruits)
• 110 meters (relevant recruits, juveniles)
• 220 meters (relevant to juveniles, adults)

# Salinity 

Maps (Salinity)

jet.colors <-colorRampPalette(c("blue", "#007FFF", "cyan","#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"))

# b <- brick(here::here('data/salinity/dd_sal_55_2000_2009.tif'))
# b.00 <- b[,,,1:365]

# select just years with study fleet bottom temps
sf.bt <- sfob.env %>% filter(year>2006 & depth > 50) 
yrs = sort(unique(sf.bt$year))    

#for(i in 1:length(yrs)){
yrmap <- function(yrs){
  sf.bt %>% filter(year == yrs) %>% 
  ggplot() + 
  geom_sf(data = US.areas %>% st_as_sf(),color = 'gray20', fill = '#cbdbcd') +
  geom_contour(data = bathy,
               aes(x=x,y=y,z=-1*z),
               breaks=c(50,100,150,200, Inf),
               size=c(0.3),
               col = 'darkgrey') +
  # stat_summary_2d(aes(x=start_lon, y=start_lat, z = bottomt),
  #                 binwidth=c(0.16666,0.16666)) + 
  #   scale_fill_gradientn(colors = jet.colors(20)) +
  coord_sf(xlim = c(-75,-65.5), ylim = c(36,44), datum = sf::st_crs(4326))  +
  labs(x = '', y = '', fill = 'Salinty') +
  theme_bw() 
}


for(i in 1:length(yrs)){
 cat("\n######",  as.character(yrs[i]),"\n")
    print(yrmap(yrs[i])) 
    cat("\n")   
}

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Salinity analysis

Currents

Cross-shelf processes may influence the retention or displacement of tilefish during early life history stages. These are explored below.

Shelf water volume

Shelf water volume: A measure of the volume of water bounded inshore of the shelf-slope front. In this analysis, shelf water is defined as all water having salinity <34 psu. The position of the shelf-slope front varies inter-annually with the higher shelf water values indicating the front being pushed further towards the shelf break.

high shv: front pushed towards sbf low shv: front pushed inshore (more slope water on shelf)

Hypothesis: Higher recruitment success correlated with years of higher shelf water volume in spring/summer. These months months may be particularly important as that is when spawning is occurring and the position of the sbf may influence the position of larvae (away from spawning grounds).

Additional variables in this dataset are shelf water temperature and salinity which may also be indicative of habitat conditions.

# Shelf water volume
shlfvol <- read.csv(here::here('data/shelf_water_volume/ShelfWaterVolume_BSB_update.csv'))

# wrangling date info, converting doy to date and month 
yrs <- as.vector(nrow(shlfvol))
shlfvol$Year <- as.character(shlfvol$Year)
for (i in 1:nrow(shlfvol)){
  yrs[i] <- strsplit(shlfvol$Year, ".", fixed = TRUE)[[i]][1]
}
shlfvol$year <- yrs
shlfvol <- shlfvol %>% mutate(date_= as.Date(Year.Day-1, 
                                             origin=paste0(year, "-01-01")), 
                              month= strftime(date_, "%m"), 
                              day=strftime(date_,"%d"), 
                              year = as.integer(year),
                              month = as.numeric(month))  


# Create shw vol by month w/lag
df.lag = shlfvol %>%
  group_by(year,month) %>% 
  summarise(mean_t = mean(ShW.T),
            mean_s = mean(ShW.S),
            mean_v = mean(ShW.Vol)) %>% 
  mutate(mean_t_lag2 = lag(mean_t,2),
         mean_t_lag3 = lag(mean_t,3), 
         mean_t_lag6 = lag(mean_t,6),
         mean_s_lag2 = lag(mean_s,2),
         mean_s_lag3 = lag(mean_s,3), 
         mean_s_lag6 = lag(mean_s,6),
         mean_v_lag2 = lag(mean_v,2),
         mean_v_lag3 = lag(mean_v,3), 
         mean_v_lag6 = lag(mean_v,6))
# Join in-situ bottom temps w/assessment recruitment estimate
df.join = dplyr::full_join(recruit, df.lag, by = join_by(year)) %>%
  dplyr::select(year, month, recruit_est, mean_t, mean_s, mean_v, 
                mean_t_lag2, mean_s_lag2, mean_v_lag2,
                mean_t_lag3, mean_s_lag3, mean_v_lag3) %>%
  tidyr::drop_na()
# See what months have data
sort(unique(df.join$month))

## [1]  7  8  9 10 11

hist(df.join$month) # will group into spring/summer fall/winter categories

## Shelf water volume no lag
ggplot2::ggplot(df.join,
                aes(x=recruit_est, y=mean_v)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean shelf water volume') +
  labs(title = 'Shelf water volume') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Shelf water temperature no lag
ggplot2::ggplot(df.join,
                aes(x=recruit_est, y=mean_t)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean shelf water temperature') +
  labs(title = 'Shelf water temperature') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Shelf water salinity no lag
ggplot2::ggplot(df.join,
                aes(x=recruit_est, y=mean_s)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean shelf water salinity') +
  labs(title = 'Shelf water salinity') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

With lags

2 Months

## Shelf water volume 2 month lag
ggplot2::ggplot(df.join,
                aes(x=recruit_est, y=mean_v_lag2)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean shelf water volume') +
  labs(title = 'Shelf water volume - lag 2 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Shelf water temperature  2 month lag
ggplot2::ggplot(df.join,
                aes(x=recruit_est, y=mean_t_lag2)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean shelf water temperature') +
  labs(title = 'Shelf water temperature - lag 2 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Shelf water salinity 2 month lag
ggplot2::ggplot(df.join,
                aes(x=recruit_est, y=mean_s_lag2)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean shelf water salinity') +
  labs(title = 'Shelf water salinity - lag 2 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

3 Months

## Shelf water volume 3 month lag
ggplot2::ggplot(df.join,
                aes(x=recruit_est, y=mean_v_lag3)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean shelf water volume') +
  labs(title = 'Shelf water volume - lag 3 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Shelf water temperature 3 month lag
ggplot2::ggplot(df.join,
                aes(x=recruit_est, y=mean_t_lag3)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean shelf water temperature') +
  labs(title = 'Shelf water temperature - lag 3 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Shelf water salinity 3 month lag
ggplot2::ggplot(df.join,
                aes(x=recruit_est, y=mean_s_lag3)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean shelf water salinity') +
  labs(title = 'Shelf water salinity - lag 3 months') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

Annual

# Create shw vol by year w/lag
df.lag = shlfvol %>%
  group_by(year) %>% 
  summarise(mean_t = mean(ShW.T),
            mean_s = mean(ShW.S),
            mean_v = mean(ShW.Vol)) %>% 
  mutate(mean_t_lag2 = lag(mean_t,2),
         mean_t_lag3 = lag(mean_t,3), 
         mean_t_lag6 = lag(mean_t,6),
         mean_s_lag2 = lag(mean_s,2),
         mean_s_lag3 = lag(mean_s,3), 
         mean_s_lag6 = lag(mean_s,6),
         mean_v_lag2 = lag(mean_v,2),
         mean_v_lag3 = lag(mean_v,3), 
         mean_v_lag6 = lag(mean_v,6))
# Join in-situ bottom temps w/assessment recruitment estimate
df.join = dplyr::full_join(recruit, df.lag, by = join_by(year)) %>%
  dplyr::select(year,recruit_est, mean_t, mean_s, mean_v, 
                mean_t_lag2, mean_s_lag2, mean_v_lag2,
                mean_t_lag3, mean_s_lag3, mean_v_lag3,
                mean_t_lag6, mean_s_lag6, mean_v_lag6) %>%
  tidyr::drop_na()

## Shelf water vol 

ggplot2::ggplot(df.join,
                aes(x=year, y=mean_v)) + 
  geom_point(color = 'black') +
  geom_line(color = 'black') +
  xlab('Year') +
  ylab('Mean shelf water volume') +
  labs(title = 'Shelf water volume') +
  theme_bw()

ggplot2::ggplot() + 
  geom_line(data = df.join, aes(x=year, y=mean_s), color = 'red') +
  geom_line(data = df.join,aes(x=year, y=mean_t*1), color = 'blue') +
  ylim(30.0,34.0) +
  scale_y_continuous(name = 'Sh.Water Salinity', 
                      sec.axis = sec_axis(~./1, name = 'Sh.Water Temperature')) + 
  xlab('Year') +
  labs(title = 'Shelf water salinity/temperature') +
  theme_bw()

ggplot2::ggplot() + 
  geom_line(data = df.join, aes(x=year, y=mean_s), color = 'red') +
  xlab('Year') +
  ylab('Mean shelf water salinity') +
  labs(title = 'Shelf water salinity') +
  theme_bw()

## Shelf water vol no lag

ggplot2::ggplot(df.join,
                aes(x=recruit_est, y=mean_v)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean shelf water volume') +
  labs(title = 'Shelf water volume') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Shelf water temperature no lag
ggplot2::ggplot(df.join,
                aes(x=recruit_est, y=mean_t)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean shelf water temperature') +
  labs(title = 'Shelf water temperature') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Shelf water salinity no lag
ggplot2::ggplot(df.join,
                aes(x=recruit_est, y=mean_s)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean shelf water salinity') +
  labs(title = 'Shelf water salinity') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

With lags

## Shelf water vol 6 yr lag

ggplot2::ggplot(df.join,
                aes(x=recruit_est, y=mean_v_lag6)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean shelf water volume') +
  labs(title = 'Shelf water volume - lag 6 years') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Shelf water temperature 6 yr lag
ggplot2::ggplot(df.join,
                aes(x=recruit_est, y=mean_t_lag6)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean shelf water temperature') +
  labs(title = 'Shelf water temperature - lag 6 years') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

## Shelf water salinity 6 yr lag
ggplot2::ggplot(df.join,
                aes(x=recruit_est, y=mean_s_lag6)) + 
  geom_point(color = 'black') +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth") +
  xlab('Recruitment estimate') +
  ylab('Mean shelf water salinity') +
  labs(title = 'Shelf water salinity - lag 6 years') +
  stat_cor(aes(label=..rr.label..)) +
  theme_bw()

Food availablity

Larval tilefish eat zooplankton, likely calanus. Calanus finmarchicus are a copepod (crustacean) with a one-year life cycle and are an important food source for many commercially important species. Calanus spp. are lipid rich, herbivorous species that eat phytoplankton, diatoms in particular (Hobbs et al. 2020).

Diatoms are often represented as microplankton (>20 µm), but many species are of the nanoplankton size class (2-20 µm), and a smaller few may even overlap with picoplanton size class (<2 µm).

Calanus

Calanus is not as common in MAB, need to figure out dominant zooplankton in MAB.

# Calanus
calanus <- ecodata::calanus_stage %>% filter(Time %in% c(1998:2021))%>% 
    rename_all(., .funs = tolower) %>% 
   mutate(year = time)


ggplot() +
  geom_line(data = calanus %>% filter(epu == 'GB', 
                                  var == 'adt-Spring'), 
       aes(x = year , y = value, col = epu), lwd = 1) + 
  geom_line(data = calanus %>% filter(epu == 'MAB', 
                                  var == 'adt-Spring'), 
       aes(x = year , y = value, col = epu), lwd = 1) +
  labs(color = c('EPU')) +
  theme_minimal()

Georges Bank

# GB
c5.gb <- calanus %>% filter(epu == 'GB', var == 'c5-Spring')
adult.gb <- calanus %>% filter(epu == 'GB', var == 'adt-Spring' )

df.c5 <- dplyr::full_join(recruit, c5.gb, by = join_by(year)) %>%
  dplyr::select(year, recruit_est, value) %>%
  tidyr::drop_na()
df.adt <- dplyr::full_join(recruit, adult.gb, by = join_by(year)) %>%
  dplyr::select(year, recruit_est, value) %>%
  tidyr::drop_na()

# Regression
ggscatter(df.c5, x = 'recruit_est', y = 'value', 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "Recruitment estimate", 
          ylab = "Calanus c5 spring (No. per 100m^-3)",
           title = 'c5')  

ggscatter(df.adt, x = 'recruit_est', y = 'value', 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "Recruitment estimate", 
          ylab = "Calanus adult spring (No. per 100m^-3)",
           title = 'Adult')  

# GLM 
eqn <- as.formula(paste('recruit_est ~', paste(colnames(df.c5)[1], 
                                               collapse = " + ")))

mod0 <- glm(recruit_est ~ 1, 
            data = df.c5, 
            family = "poisson")

mod1 <- glm(eqn, 
            data = df.c5, 
            family = "poisson")

summary(mod0)

## 
## Call:
## glm(formula = recruit_est ~ 1, family = "poisson", data = df.c5)
## 
## Coefficients:
##               Estimate Std. Error z value            Pr(>|z|)    
## (Intercept) 14.1979354  0.0001802   78775 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 8933474  on 20  degrees of freedom
## Residual deviance: 8933474  on 20  degrees of freedom
## AIC: 8933809
## 
## Number of Fisher Scoring iterations: 4

summary(mod1)

## 
## Call:
## glm(formula = eqn, family = "poisson", data = df.c5)
## 
## Coefficients:
##                Estimate  Std. Error z value            Pr(>|z|)    
## (Intercept) 43.50429247  0.05804867   749.4 <0.0000000000000002 ***
## year        -0.01459584  0.00002891  -504.8 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 8933474  on 20  degrees of freedom
## Residual deviance: 8677368  on 19  degrees of freedom
## AIC: 8677705
## 
## Number of Fisher Scoring iterations: 4

AIC(mod0, mod1) %>% dplyr::arrange(AIC)

##      df     AIC
## mod1  2 8677705
## mod0  1 8933809

null_prediction <- exp(predict(mod0))
mod_prediction <- exp(predict(mod1))


plot(df.c5$year, df.c5$recruit_est, type = 'l')
lines(df.c5$year, null_prediction, col = "gray")
lines(df.c5$year, mod_prediction, col = "red")

Mid-atlantic

# Mid-Atlantic Bight
c5.mab <- calanus %>% filter(epu == 'MAB', var == 'c5-Spring')
adult.mab <- calanus %>% filter(epu == 'MAB', var == 'adt-Spring' )

df.c5 <- dplyr::full_join(recruit, c5.mab, by = join_by(year)) %>%
  dplyr::select(year, recruit_est, value) %>%
  tidyr::drop_na()
df.adt <- dplyr::full_join(recruit, adult.mab, by = join_by(year)) %>%
  dplyr::select(year, recruit_est, value) %>%
  tidyr::drop_na()

# Regression
ggscatter(df.c5, x = 'recruit_est', y = 'value', 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "Recruitment estimate", 
          ylab = "Calanus c5 spring (No. per 100m^-3)",
           title = 'c5')  

ggscatter(df.adt, x = 'recruit_est', y = 'value', 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "Recruitment estimate", 
          ylab = "Calanus adult spring (No. per 100m^-3)",
           title = 'Adult')  

Microplankton

Microplankton maps

# microplankton

Microplankton analysis

# Microplankton

Chlorophyll-A

# CHL-A

Maps (Chlorophyll-A)

# CHL-A

Chlorophyll-A analysis

# CHL-A

SST Fronts

# SST fronts

Maps (SST fronts)

SST Fronts analysis

References:

Hobbs, L., Banas, N. S., Cottier, F. R., Berge, J., & Daase, M. (2020). Eat or sleep: availability of winter prey explains mid-winter and spring activity in an Arctic Calanus population. Frontiers in Marine Science, 7, 541564.